| Peer-Reviewed

Edge Detection of an Image Based on Extended Difference of Gaussian

Received: 24 November 2019     Accepted: 9 December 2019     Published: 20 December 2019
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Abstract

Edge detection includes a variety of mathematical methods that aim at identifying points in a digital image at which the image brightness changes sharply or, more formally, has discontinuities. The points at which image brightness changes sharply are typically organized into a set of curved line segments termed edges. The same problem of finding discontinuities in one-dimensional signals is known as step detection and the problem of finding signal discontinuities over time is known as change detection. Edge detection is a fundamental tool in image processing, machine vision and computer vision, particularly in the areas of feature detection and feature extraction. It's also the most important parts of image processing, especially in determining the image quality. There are many different techniques to evaluate the quality of the image. The most commonly used technique is pixel based difference measures which include peak signal to noise ratio (PSNR), signal to noise ratio (SNR), mean square error (MSE), similarity structure index mean (SSIM) and normalized absolute error (NAE).... etc. This paper study and detect the edges using extended difference of Gaussian filter applied on many of different images with different sizes, then measure the quality images using the PSNR, MSE, NAE and the time in seconds.

Published in American Journal of Computer Science and Technology (Volume 2, Issue 3)
DOI 10.11648/j.ajcst.20190203.11
Page(s) 35-47
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2019. Published by Science Publishing Group

Keywords

Edge Detection, Image Quality, Gaussian Filter, Extended Difference of Gaussian, Peak Signal to Ratio, Mean Square Error

References
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Cite This Article
  • APA Style

    Hameda Abd El-Fattah El-Sennary, Mohamed Eid Hussien, Abd El-Mgeid Amin Ali. (2019). Edge Detection of an Image Based on Extended Difference of Gaussian. American Journal of Computer Science and Technology, 2(3), 35-47. https://doi.org/10.11648/j.ajcst.20190203.11

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    ACS Style

    Hameda Abd El-Fattah El-Sennary; Mohamed Eid Hussien; Abd El-Mgeid Amin Ali. Edge Detection of an Image Based on Extended Difference of Gaussian. Am. J. Comput. Sci. Technol. 2019, 2(3), 35-47. doi: 10.11648/j.ajcst.20190203.11

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    AMA Style

    Hameda Abd El-Fattah El-Sennary, Mohamed Eid Hussien, Abd El-Mgeid Amin Ali. Edge Detection of an Image Based on Extended Difference of Gaussian. Am J Comput Sci Technol. 2019;2(3):35-47. doi: 10.11648/j.ajcst.20190203.11

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  • @article{10.11648/j.ajcst.20190203.11,
      author = {Hameda Abd El-Fattah El-Sennary and Mohamed Eid Hussien and Abd El-Mgeid Amin Ali},
      title = {Edge Detection of an Image Based on Extended Difference of Gaussian},
      journal = {American Journal of Computer Science and Technology},
      volume = {2},
      number = {3},
      pages = {35-47},
      doi = {10.11648/j.ajcst.20190203.11},
      url = {https://doi.org/10.11648/j.ajcst.20190203.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajcst.20190203.11},
      abstract = {Edge detection includes a variety of mathematical methods that aim at identifying points in a digital image at which the image brightness changes sharply or, more formally, has discontinuities. The points at which image brightness changes sharply are typically organized into a set of curved line segments termed edges. The same problem of finding discontinuities in one-dimensional signals is known as step detection and the problem of finding signal discontinuities over time is known as change detection. Edge detection is a fundamental tool in image processing, machine vision and computer vision, particularly in the areas of feature detection and feature extraction. It's also the most important parts of image processing, especially in determining the image quality. There are many different techniques to evaluate the quality of the image. The most commonly used technique is pixel based difference measures which include peak signal to noise ratio (PSNR), signal to noise ratio (SNR), mean square error (MSE), similarity structure index mean (SSIM) and normalized absolute error (NAE).... etc. This paper study and detect the edges using extended difference of Gaussian filter applied on many of different images with different sizes, then measure the quality images using the PSNR, MSE, NAE and the time in seconds.},
     year = {2019}
    }
    

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  • TY  - JOUR
    T1  - Edge Detection of an Image Based on Extended Difference of Gaussian
    AU  - Hameda Abd El-Fattah El-Sennary
    AU  - Mohamed Eid Hussien
    AU  - Abd El-Mgeid Amin Ali
    Y1  - 2019/12/20
    PY  - 2019
    N1  - https://doi.org/10.11648/j.ajcst.20190203.11
    DO  - 10.11648/j.ajcst.20190203.11
    T2  - American Journal of Computer Science and Technology
    JF  - American Journal of Computer Science and Technology
    JO  - American Journal of Computer Science and Technology
    SP  - 35
    EP  - 47
    PB  - Science Publishing Group
    SN  - 2640-012X
    UR  - https://doi.org/10.11648/j.ajcst.20190203.11
    AB  - Edge detection includes a variety of mathematical methods that aim at identifying points in a digital image at which the image brightness changes sharply or, more formally, has discontinuities. The points at which image brightness changes sharply are typically organized into a set of curved line segments termed edges. The same problem of finding discontinuities in one-dimensional signals is known as step detection and the problem of finding signal discontinuities over time is known as change detection. Edge detection is a fundamental tool in image processing, machine vision and computer vision, particularly in the areas of feature detection and feature extraction. It's also the most important parts of image processing, especially in determining the image quality. There are many different techniques to evaluate the quality of the image. The most commonly used technique is pixel based difference measures which include peak signal to noise ratio (PSNR), signal to noise ratio (SNR), mean square error (MSE), similarity structure index mean (SSIM) and normalized absolute error (NAE).... etc. This paper study and detect the edges using extended difference of Gaussian filter applied on many of different images with different sizes, then measure the quality images using the PSNR, MSE, NAE and the time in seconds.
    VL  - 2
    IS  - 3
    ER  - 

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Author Information
  • Faculty of Science, Aswan University, Aswan, Egypt

  • Faculty of Science, Aswan University, Aswan, Egypt

  • Faculty of Computers and Information, Minia University, Minia, Egypt

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